Perturbation theory for modeling galaxy bias: validation with simulations of the Dark Energy Survey. (arXiv:2008.05991v1 [astro-ph.CO])
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We describe perturbation theory (PT) models of galaxy bias for applications
to photometric galaxy surveys. We model the galaxy-galaxy and galaxy-matter
correlation functions in configuration space and validate against measurements
from mock catalogs designed for the Dark Energy Survey (DES). We find that an
effective PT model with five galaxy bias parameters provides a good description
of the 3D correlation functions above scales of 4 Mpc/$h$ and $z < 1$. Our
tests show that at the projected precision of the DES-Year 3 analysis, two of
the non-linear bias parameters can be fixed to their co-evolution values, and a
third (the $k^2$ term for higher derivative bias) set to zero. The agreement is
typically at the 2 percent level over scales of interest, which is the
statistical uncertainty of our simulation measurements. To achieve this level
of agreement, our {it fiducial} model requires using the full non-linear
matter power spectrum (rather than the 1-loop PT one). We also measure the
relationship between the non-linear and linear bias parameters and compare them
to their expected co-evolution values. We use these tests to motivate the
galaxy bias model and scale cuts for the cosmological analysis of the Dark
Energy Survey; our conclusions are generally applicable to all photometric
surveys.
We describe perturbation theory (PT) models of galaxy bias for applications
to photometric galaxy surveys. We model the galaxy-galaxy and galaxy-matter
correlation functions in configuration space and validate against measurements
from mock catalogs designed for the Dark Energy Survey (DES). We find that an
effective PT model with five galaxy bias parameters provides a good description
of the 3D correlation functions above scales of 4 Mpc/$h$ and $z < 1$. Our
tests show that at the projected precision of the DES-Year 3 analysis, two of
the non-linear bias parameters can be fixed to their co-evolution values, and a
third (the $k^2$ term for higher derivative bias) set to zero. The agreement is
typically at the 2 percent level over scales of interest, which is the
statistical uncertainty of our simulation measurements. To achieve this level
of agreement, our {it fiducial} model requires using the full non-linear
matter power spectrum (rather than the 1-loop PT one). We also measure the
relationship between the non-linear and linear bias parameters and compare them
to their expected co-evolution values. We use these tests to motivate the
galaxy bias model and scale cuts for the cosmological analysis of the Dark
Energy Survey; our conclusions are generally applicable to all photometric
surveys.
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